1. Field of the Invention
The present invention relates to a technology for extracting a target object region from picture data that include a region extraction target image, based on attributes of pixels of a picture; particularly, the invention relates to a picture region extraction method and device that extract a region such as a specific organ, tumor, cell nucleus, and glandular cavity in a picture from picture data taken with an MRI apparatus or a CT scanner, or a cross-sectional view of a biological tissue observed with a biological microscope.
2. Description of the Related Art
The following method of the prior art has been known for extracting an image of a specific object from a picture.
(for example, Japanese Patent laid-open (Kokai) No. 2001-92980; hereinafter referred to as Document 1.))
In Document 1, the configuration is such that, from picture data containing the image of an image to be subjected to contour extraction, the regions to which each point belongs are separated and the boundaries between the regions are extracted as contours, based on the attribute of each point of the picture and using region membership probability, which is the probability that each of the points on the picture belongs to each of the regions. By adopting such a configuration, the contour of each region can be automatically extracted, and the extraction of the contour region can be accelerated, without explicitly setting a threshold for region classification.
In the extraction of the contour region in Document 1, the expected value of the region membership probability is calculated from picture data, which is the probability for each point on a picture that it belongs to each of the regions, an evaluation function is calculated based on the mixture probability distribution determined from the region membership probability and a region parameter, each region is separated based on the region membership probability, and the contour is extracted based on the separated regions, therefore, when determining the evaluation function, it is necessary to obtain a sum for all the picture points (pixels), so that the calculation of the evaluation function has to be repeated several times in order to determine an optimal parameter.
Therefore, the conventional method has the problem that enormous amounts of time are needed to extract the regions as the size of the picture becomes large.